895 research outputs found
Challenges for the comprehensive management of cloud services in a PaaS framework
The 4CaaSt project aims at developing a PaaS framework that enables flexible definition, marketing, deployment and management of Cloud-based services and applications. The major innovations proposed by 4CaaSt are the blueprint and its lifecycle management, a one stop shop for Cloud services and a PaaS level resource management featuring elasticity. 4CaaSt also provides a portfolio of ready to use Cloud native services and Cloud-aware immigrant technologies
Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions
Traditional power grids are being transformed into Smart Grids (SGs) to
address the issues in existing power system due to uni-directional information
flow, energy wastage, growing energy demand, reliability and security. SGs
offer bi-directional energy flow between service providers and consumers,
involving power generation, transmission, distribution and utilization systems.
SGs employ various devices for the monitoring, analysis and control of the
grid, deployed at power plants, distribution centers and in consumers' premises
in a very large number. Hence, an SG requires connectivity, automation and the
tracking of such devices. This is achieved with the help of Internet of Things
(IoT). IoT helps SG systems to support various network functions throughout the
generation, transmission, distribution and consumption of energy by
incorporating IoT devices (such as sensors, actuators and smart meters), as
well as by providing the connectivity, automation and tracking for such
devices. In this paper, we provide a comprehensive survey on IoT-aided SG
systems, which includes the existing architectures, applications and prototypes
of IoT-aided SG systems. This survey also highlights the open issues,
challenges and future research directions for IoT-aided SG systems
Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Architectures
Cloud controllers support the operation and quality management of dynamic cloud architectures by automatically scaling the compute resources to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of architecture adaptation rules. However, for a cloud provider, deployed application architectures are black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. We propose the dynamic learning of adaptation rules for deployed application architectures in the cloud. We introduce FQL4KE, a self-learning fuzzy controller that learns and modifies fuzzy rules at runtime. The benefit is that we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire. FQL4KE empowers users to configure cloud controllers by simply adjusting weights representing priorities for architecture quality instead of defining complex rules. FQL4KE has been experimentally validated using the cloud application framework ElasticBench in Azure and OpenStack. The experimental results demonstrate that FQL4KE outperforms both a fuzzy controller without learning and the native Azure auto-scalin
Building an Expert System for Evaluation of Commercial Cloud Services
Commercial Cloud services have been increasingly supplied to customers in
industry. To facilitate customers' decision makings like cost-benefit analysis
or Cloud provider selection, evaluation of those Cloud services are becoming
more and more crucial. However, compared with evaluation of traditional
computing systems, more challenges will inevitably appear when evaluating
rapidly-changing and user-uncontrollable commercial Cloud services. This paper
proposes an expert system for Cloud evaluation that addresses emerging
evaluation challenges in the context of Cloud Computing. Based on the knowledge
and data accumulated by exploring the existing evaluation work, this expert
system has been conceptually validated to be able to give suggestions and
guidelines for implementing new evaluation experiments. As such, users can
conveniently obtain evaluation experiences by using this expert system, which
is essentially able to make existing efforts in Cloud services evaluation
reusable and sustainable.Comment: 8 page, Proceedings of the 2012 International Conference on Cloud and
Service Computing (CSC 2012), pp. 168-175, Shanghai, China, November 22-24,
201
A Design Science Research Methodology for Microservice Architecture and System Research
As enterprise continue their Digital journey, Monolithic architecture approach of building Digital platforms has now proven to be inefficient and obsolete. Architectural paradigms in software development are changing with the spinning of time. The paradigms of architecture, formerly considered sufficiently well architecture and even dominant over the years, are now referred to as monolithic. The demands for fresh technology approaches are continuously evolving to cope the new set of business challenges. [25] The purpose of this thesis is to evaluate the approach with an experiment in designing a microservice system. The thesis motivates, presents, demonstrates in use, and evaluates a methodology in microservice system for conducting Design Science (DS) research. Moreover, the thesis will go through in detail description of Microservice architecture and enables us to differentiate and find the right Software Development Methodology (SDM) for the Digital platform. SDM enables the proper management of the software development processes, the project team, products and services in terms of cost effectiveness, time, and quality. [23] The objective of this thesis is to investigate the differences in between architectural paradigms such as monolithic, cloud native and microservice and find the appropriate paradigm that satisfy the enterprises for continuing their digital business. The research of using different platforms and environment for possible improvement on the software development process that enables easy to develop, run and ship distributed application easily and anywhere will be carried out. Similarly, research also focuses on maintaining the development environment consistent, testable and maintainable and hosting the application to the cloud irrespective to underling infrastructure and operation system. The research artifacts will help the enterprises and stakeholders to take an important decision in the selection of the architectural paradigm for their digital platform in advance. The paper concludes that, Microservice architecture is one of the well-known SDM suitable for large enterprise software business application
Leveraging Market Research Techniques in IS: A Review and Framework of Conjoint Analysis Studies in the IS Discipline
With cloud and mobile computing, information systems (IS) have evolved towards mass-market services. While IS success requires user involvement, the IS discipline lacks methods that allow organizations to integrate the “voice of the customer” into mass-market services with individual and dispersed users. Conjoint analysis (CA), from marketing research, provides insights into user preferences and measures user trade-offs for multiple product features simultaneously. While CA has gained popularity in the IS domain, existing studies have mostly been one-time efforts, which has resulted in little knowledge accumulation about CA’s applications in IS. We argue that CA could have a significant impact on IS research (and practice) if this method was further developed and adopted for IS application areas. From reviewing 70 CA studies published between 1999 and 2019 in the IS discipline, we found that CA supports in initially conceptualizing, iteratively designing, and evaluating IS and their business models. We critically assess the methodological choices along the CA procedure to provide recommendations and guidance on “how” to leverage CA techniques in future IS research. We then synthesize our findings into a framework for conjoint analysis studies in IS that outlines “where” researchers and practitioners can apply CA along the IS lifecycle
Leveraging Market Research Techniques in IS – A Review of Conjoint Analysis in IS Research
With the increasing importance of mass-market information systems (IS), understanding individual user preferences for IS design and adoption is essential. However, this has been a challenging task due to the complexity of balancing functional, non-functional, and economic requirements. Conjoint analysis (CA), a marketing research technique, estimates user preferences by measuring tradeoffs between products attributes. Although the number of studies applying CA in IS has increased in the past years, we still lack fundamental discussion on its use in our discipline. We review the existing CA studies in IS with regard to the application areas and methodological choices along the CA procedure. Based on this review, we develop a reference framework for application areas in IS that serves as foundation for future studies. We argue that CA can be leveraged in requirements management, business model design, and systems evaluation. As future research opportunities, we see domain-specific adaptations e.g., user preference models
Service Prototyping Lab Report - 2017 (Y2)
The annual activity report of the Service Prototyping Lab at Zurich University of Applied Sciences. Research trends and initiatives, research projects, transfer to education and local industry, academic community involvement, qualification and scientific development over the period of one year are among the covered topics
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